Nguyen Nga T T, Kenyon Garrett T, Yoon Boram
CCS-3, Information Sciences, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
New Mexico Consortium, Los Alamos, NM, 87545, USA.
Sci Rep. 2020 Jul 2;10(1):10915. doi: 10.1038/s41598-020-67769-x.
We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer. In this regression algorithm, we concatenate the independent and dependent variables as a combined vector, and encode the high-order correlations between them into a dictionary optimized for sparse reconstruction. On a test dataset, the dependent variable is initialized to its average value and then a sparse reconstruction of the combined vector is obtained in which the dependent variable is typically shifted closer to its true value, as in a standard inpainting or denoising task. Here, a quantum annealer, which can presumably exploit a fully entangled initial state to better explore the complex energy landscape, is used to solve the highly non-convex sparse coding optimization problem. The regression algorithm is demonstrated for a lattice quantum chromodynamics simulation data using a D-Wave 2000Q quantum annealer and good prediction performance is achieved. The regression test is performed using six different values for the number of fully connected logical qubits, between 20 and 64. The scaling results indicate that a larger number of qubits gives better prediction accuracy.
我们提出了一种回归算法，该算法利用为在D-Wave量子退火器上进行稀疏推理而优化的学习字典。在这种回归算法中，我们将自变量和因变量连接成一个组合向量，并将它们之间的高阶相关性编码到一个为稀疏重建而优化的字典中。在测试数据集上，因变量被初始化为其平均值，然后获得组合向量的稀疏重建，其中因变量通常会更接近其真实值，就像在标准的图像修复或去噪任务中一样。在这里，量子退火器大概可以利用完全纠缠的初始状态来更好地探索复杂的能量景观，用于解决高度非凸的稀疏编码优化问题。使用D-Wave 2000Q量子退火器对格点量子色动力学模拟数据演示了该回归算法，并取得了良好的预测性能。使用20到64之间的六个不同的全连接逻辑量子比特数进行回归测试。缩放结果表明，更多的量子比特能带来更好的预测精度。
